Poll Results: ETL Tools for Data Integration

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Lyftrondata conducted a poll to know whether or not businesses use ETL for data integration. 80% of people agreed. Read more!
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Today, business data requirements have increased manifold while the changes in market dynamics are happening at a lightning speed. Businesses need to be ready to handle such fast-paced changes in their market and make rapid decisions to remain competitive. But, most of them are stuck with their current ETL because it is nowhere close to what they want it to be. To manage the changing market dynamics, they need a modern and agile data integration solution that can easily integrate all those disconnected systems and help them make faster decisions for the betterment of their business.
To take a deeper dive in this situation, Lyftrondata conducted a poll to know whether or not businesses use ETL for data integration. 80% of people agreed.

What is ETL

ETL tools automatically extract, transform and load data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.
As popularity of databases grew in the 1970s, ETL (Extract, Transform and Load) tools were developed for integrating and loading data for computation and analysis, becoming the primary method to process data for data warehousing projects.
ETL tools provide the foundation for data analytics and machine learning workstreams. Business rules applied to data through ETL cleanse and organize it so that business intelligence needs can be addressed, like monthly reporting, but also more advanced analytics can be used.

What is Data Integration

Data integration is the process of combining diverse data into a single, unified view. Any number of steps can be involved in data integration, including ingestion, cleansing, ETL mapping and transformation. The method begins with ingestion and includes steps such as cleansing, ETL mapping, and transformation. Eventually, providing analytics tools with meaningful information.
There is no one-size-fits-all data integration solution. However, most solutions under this umbrella involve three main components: a network of data sources, a master server, and clients accessing data from the master server.
In most data integration processes, client requests are sent to master servers for data. The master servers then intake the needed data from internal and external sources. The data is extracted from these sources, consolidated into a single, cohesive data set, and served back.

Lyftrondata Vs Other ETL, Data Pipeline and Data Virtualization Tools

What exactly is Lyftrondata

Lyftrondata is a modern data platform that provides real-time access to any data and enables users to query them with simple ANSI SQL. With Lyftrondata, enterprises can build data pipelines in minutes and shorten the time to insights by 75% with the power of the modern cloud compute of Snowflake and Spark.
The modern data platform eliminates the time spent by engineers building data pipelines manually and makes data instantly accessible by providing real-time access with simple ANSI SQL. Lyftrondata’s pre-built connectors automatically deliver data to warehouses in normalized, ready-to query schemas and provide full search on data catalog. Whether you want to build a governed data lake, data warehouse, or simply want to migrate from your traditional database to a modern cloud data warehouse, do it all with Lyftrondata. Simply create and manage all your data workloads on one platform by automatically building your pipeline and warehouse. Analyze it instantly with ANSI SQL, BI/ML tools, and share it without worrying about writing any custom code. Boost the productivity of your data professionals and shorten your time to value. Lyftrondata helps to automatically normalize API/JSON/XML/S3/Blob/NoSql sources into a ready-to-query relational format.

Why Lyftrondata is the most preferred ETL tool for data-driven enterprises.

Lyftrondata is counted among the top ETL tools for 2021 owing to its astute ability to streamline data management securely and rapidly from various data sources. The robust architecture of this ETL tool helps find a balance between traditional ETL-heavy data preparation and agility to find, connect and use any data without delays.

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